# Adaptive Detrending to Accelerate Convolutional Gated Recurrent Unit Training for Contextual Video Recognition

## TL;DR

This paper introduces adaptive detrending, a novel temporal normalization technique that accelerates training and enhances generalization of convolutional gated recurrent units for contextual video recognition, addressing long-term video understanding challenges.

## Contribution

The paper proposes adaptive detrending (AD), a new method for temporal normalization that speeds up ConvGRU training and improves performance in contextual video recognition tasks.

## Key findings

- ConvGRU outperforms feed-forward neural networks in contextual recognition.
- AD significantly accelerates ConvGRU training and improves generalization.
- Combining AD with existing normalization methods yields further improvements.

## Abstract

Based on the progress of image recognition, video recognition has been extensively studied recently. However, most of the existing methods are focused on short-term but not long-term video recognition, called contextual video recognition. To address contextual video recognition, we use convolutional recurrent neural networks (ConvRNNs) having a rich spatio-temporal information processing capability, but ConvRNNs requires extensive computation that slows down training. In this paper, inspired by the normalization and detrending methods, we propose adaptive detrending (AD) for temporal normalization in order to accelerate the training of ConvRNNs, especially for convolutional gated recurrent unit (ConvGRU). AD removes internal covariate shift within a sequence of each neuron in recurrent neural networks (RNNs) by subtracting a trend. In the experiments for contextual recognition on ConvGRU, the results show that (1) ConvGRU clearly outperforms the feed-forward neural networks, (2) AD consistently offers a significant training acceleration and generalization improvement, and (3) AD is further improved by collaborating with the existing normalization methods.